Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations50
Missing cells101
Missing cells (%)10.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.6 KiB
Average record size in memory666.8 B

Variable types

Text4
Numeric10
Categorical5
DateTime1

Alerts

alcohol_consumption is highly overall correlated with blood_pressure_systolic and 2 other fieldsHigh correlation
blood_pressure_diastolic is highly overall correlated with blood_pressure_systolic and 9 other fieldsHigh correlation
blood_pressure_systolic is highly overall correlated with alcohol_consumption and 10 other fieldsHigh correlation
blood_type is highly overall correlated with current_insurance_providerHigh correlation
bmi is highly overall correlated with alcohol_consumption and 10 other fieldsHigh correlation
cholesterol_total is highly overall correlated with blood_pressure_diastolic and 9 other fieldsHigh correlation
current_insurance_provider is highly overall correlated with blood_typeHigh correlation
exercise_frequency_per_week is highly overall correlated with blood_pressure_diastolic and 8 other fieldsHigh correlation
fitness_level is highly overall correlated with blood_pressure_diastolic and 7 other fieldsHigh correlation
glucose_level is highly overall correlated with blood_pressure_diastolic and 8 other fieldsHigh correlation
height_cm is highly overall correlated with bmi and 1 other fieldsHigh correlation
resting_heart_rate is highly overall correlated with blood_pressure_diastolic and 8 other fieldsHigh correlation
sleep_hours_avg is highly overall correlated with blood_pressure_diastolic and 9 other fieldsHigh correlation
smoking_status is highly overall correlated with blood_pressure_diastolic and 9 other fieldsHigh correlation
weight_kg is highly overall correlated with alcohol_consumption and 7 other fieldsHigh correlation
smoking_status is highly imbalanced (50.3%) Imbalance
medical_conditions has 34 (68.0%) missing values Missing
allergies has 31 (62.0%) missing values Missing
medications has 36 (72.0%) missing values Missing
user_id has unique values Unique

Reproduction

Analysis started2025-06-03 14:23:41.967692
Analysis finished2025-06-03 14:23:48.524353
Duration6.56 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

user_id
Text

Unique 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2025-06-03T16:23:48.657862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters300
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowUSR001
2nd rowUSR002
3rd rowUSR003
4th rowUSR004
5th rowUSR005
ValueCountFrequency (%)
usr001 1
 
2.0%
usr002 1
 
2.0%
usr003 1
 
2.0%
usr004 1
 
2.0%
usr005 1
 
2.0%
usr006 1
 
2.0%
usr007 1
 
2.0%
usr008 1
 
2.0%
usr009 1
 
2.0%
usr010 1
 
2.0%
Other values (40) 40
80.0%
2025-06-03T16:23:48.878519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 64
21.3%
U 50
16.7%
S 50
16.7%
R 50
16.7%
1 15
 
5.0%
2 15
 
5.0%
3 15
 
5.0%
4 15
 
5.0%
5 6
 
2.0%
6 5
 
1.7%
Other values (3) 15
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 64
21.3%
U 50
16.7%
S 50
16.7%
R 50
16.7%
1 15
 
5.0%
2 15
 
5.0%
3 15
 
5.0%
4 15
 
5.0%
5 6
 
2.0%
6 5
 
1.7%
Other values (3) 15
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 64
21.3%
U 50
16.7%
S 50
16.7%
R 50
16.7%
1 15
 
5.0%
2 15
 
5.0%
3 15
 
5.0%
4 15
 
5.0%
5 6
 
2.0%
6 5
 
1.7%
Other values (3) 15
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 64
21.3%
U 50
16.7%
S 50
16.7%
R 50
16.7%
1 15
 
5.0%
2 15
 
5.0%
3 15
 
5.0%
4 15
 
5.0%
5 6
 
2.0%
6 5
 
1.7%
Other values (3) 15
 
5.0%

height_cm
Real number (ℝ)

High correlation 

Distinct21
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.38
Minimum162
Maximum183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:23:48.940889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum162
5-th percentile163.45
Q1166.25
median173
Q3178
95-th percentile181.55
Maximum183
Range21
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation6.3370952
Coefficient of variation (CV)0.036762358
Kurtosis-1.4240427
Mean172.38
Median Absolute Deviation (MAD)6
Skewness-0.0096074508
Sum8619
Variance40.158776
MonotonicityNot monotonic
2025-06-03T16:23:49.007129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
165 4
 
8.0%
178 3
 
6.0%
170 3
 
6.0%
175 3
 
6.0%
168 3
 
6.0%
164 3
 
6.0%
180 3
 
6.0%
176 3
 
6.0%
166 3
 
6.0%
177 3
 
6.0%
Other values (11) 19
38.0%
ValueCountFrequency (%)
162 1
 
2.0%
163 2
4.0%
164 3
6.0%
165 4
8.0%
166 3
6.0%
167 2
4.0%
168 3
6.0%
169 2
4.0%
170 3
6.0%
171 1
 
2.0%
ValueCountFrequency (%)
183 1
 
2.0%
182 2
4.0%
181 2
4.0%
180 3
6.0%
179 3
6.0%
178 3
6.0%
177 3
6.0%
176 3
6.0%
175 3
6.0%
174 2
4.0%

weight_kg
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.66
Minimum55
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:23:49.057693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile56
Q159
median66.5
Q378
95-th percentile84
Maximum85
Range30
Interquartile range (IQR)19

Descriptive statistics

Standard deviation10.235213
Coefficient of variation (CV)0.14907098
Kurtosis-1.6018061
Mean68.66
Median Absolute Deviation (MAD)8.5
Skewness0.19034946
Sum3433
Variance104.75959
MonotonicityNot monotonic
2025-06-03T16:23:49.124418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
58 5
 
10.0%
61 3
 
6.0%
78 3
 
6.0%
59 3
 
6.0%
60 3
 
6.0%
72 2
 
4.0%
76 2
 
4.0%
55 2
 
4.0%
63 2
 
4.0%
85 2
 
4.0%
Other values (16) 23
46.0%
ValueCountFrequency (%)
55 2
 
4.0%
56 2
 
4.0%
57 2
 
4.0%
58 5
10.0%
59 3
6.0%
60 3
6.0%
61 3
6.0%
62 2
 
4.0%
63 2
 
4.0%
64 1
 
2.0%
ValueCountFrequency (%)
85 2
4.0%
84 2
4.0%
83 1
 
2.0%
82 2
4.0%
81 1
 
2.0%
80 2
4.0%
79 2
4.0%
78 3
6.0%
77 1
 
2.0%
76 2
4.0%

bmi
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.966
Minimum20.7
Maximum26.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:23:49.178855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20.7
5-th percentile20.845
Q121.325
median22.25
Q324.2
95-th percentile25.955
Maximum26.8
Range6.1
Interquartile range (IQR)2.875

Descriptive statistics

Standard deviation1.879493
Coefficient of variation (CV)0.081838064
Kurtosis-1.2018576
Mean22.966
Median Absolute Deviation (MAD)1.3
Skewness0.49842495
Sum1148.3
Variance3.5324939
MonotonicityNot monotonic
2025-06-03T16:23:49.241228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
21.3 5
 
10.0%
21.5 4
 
8.0%
21.4 3
 
6.0%
21.6 3
 
6.0%
25.9 2
 
4.0%
24.1 2
 
4.0%
20.8 2
 
4.0%
21 2
 
4.0%
25.5 2
 
4.0%
24.2 2
 
4.0%
Other values (23) 23
46.0%
ValueCountFrequency (%)
20.7 1
 
2.0%
20.8 2
 
4.0%
20.9 1
 
2.0%
21 2
 
4.0%
21.1 1
 
2.0%
21.2 1
 
2.0%
21.3 5
10.0%
21.4 3
6.0%
21.5 4
8.0%
21.6 3
6.0%
ValueCountFrequency (%)
26.8 1
2.0%
26.5 1
2.0%
26 1
2.0%
25.9 2
4.0%
25.7 1
2.0%
25.6 1
2.0%
25.5 2
4.0%
25.2 1
2.0%
24.9 1
2.0%
24.5 1
2.0%

blood_type
Categorical

High correlation 

Distinct8
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
A+
O+
A-
O-
AB+
Other values (3)
14 

Length

Max length3
Median length2
Mean length2.18
Min length2

Characters and Unicode

Total characters109
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA+
2nd rowO-
3rd rowB+
4th rowAB+
5th rowA-

Common Values

ValueCountFrequency (%)
A+ 9
18.0%
O+ 8
16.0%
A- 7
14.0%
O- 7
14.0%
AB+ 5
10.0%
B+ 5
10.0%
B- 5
10.0%
AB- 4
8.0%

Length

2025-06-03T16:23:49.323800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T16:23:49.374326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 16
32.0%
o 15
30.0%
b 10
20.0%
ab 9
18.0%

Most occurring characters

ValueCountFrequency (%)
+ 27
24.8%
A 25
22.9%
- 23
21.1%
B 19
17.4%
O 15
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
+ 27
24.8%
A 25
22.9%
- 23
21.1%
B 19
17.4%
O 15
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
+ 27
24.8%
A 25
22.9%
- 23
21.1%
B 19
17.4%
O 15
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
+ 27
24.8%
A 25
22.9%
- 23
21.1%
B 19
17.4%
O 15
13.8%

medical_conditions
Text

Missing 

Distinct15
Distinct (%)93.8%
Missing34
Missing (%)68.0%
Memory size2.3 KiB
2025-06-03T16:23:49.491034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length12
Min length6

Characters and Unicode

Total characters192
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)87.5%

Sample

1st rowHypertension
2nd rowDiabetes Type 2
3rd rowAsthma
4th rowLower back pain
5th rowHypothyroidism
ValueCountFrequency (%)
hypothyroidism 2
 
8.3%
diabetes 1
 
4.2%
type 1
 
4.2%
2 1
 
4.2%
hypertension 1
 
4.2%
asthma 1
 
4.2%
lower 1
 
4.2%
back 1
 
4.2%
pain 1
 
4.2%
knee 1
 
4.2%
Other values (13) 13
54.2%
2025-06-03T16:23:49.661240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 24
12.5%
i 20
 
10.4%
s 16
 
8.3%
o 13
 
6.8%
a 13
 
6.8%
r 12
 
6.2%
t 11
 
5.7%
p 9
 
4.7%
8
 
4.2%
y 8
 
4.2%
Other values (24) 58
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 24
12.5%
i 20
 
10.4%
s 16
 
8.3%
o 13
 
6.8%
a 13
 
6.8%
r 12
 
6.2%
t 11
 
5.7%
p 9
 
4.7%
8
 
4.2%
y 8
 
4.2%
Other values (24) 58
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 24
12.5%
i 20
 
10.4%
s 16
 
8.3%
o 13
 
6.8%
a 13
 
6.8%
r 12
 
6.2%
t 11
 
5.7%
p 9
 
4.7%
8
 
4.2%
y 8
 
4.2%
Other values (24) 58
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 24
12.5%
i 20
 
10.4%
s 16
 
8.3%
o 13
 
6.8%
a 13
 
6.8%
r 12
 
6.2%
t 11
 
5.7%
p 9
 
4.7%
8
 
4.2%
y 8
 
4.2%
Other values (24) 58
30.2%

allergies
Text

Missing 

Distinct15
Distinct (%)78.9%
Missing31
Missing (%)62.0%
Memory size2.3 KiB
2025-06-03T16:23:49.761132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.1052632
Min length3

Characters and Unicode

Total characters116
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)63.2%

Sample

1st rowPollen
2nd rowShellfish
3rd rowNuts
4th rowLatex
5th rowDust mites
ValueCountFrequency (%)
pollen 3
15.0%
latex 2
 
10.0%
gluten 2
 
10.0%
dust 2
 
10.0%
nuts 1
 
5.0%
mites 1
 
5.0%
cats 1
 
5.0%
shellfish 1
 
5.0%
soy 1
 
5.0%
penicillin 1
 
5.0%
Other values (5) 5
25.0%
2025-06-03T16:23:49.924420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 13
11.2%
e 12
 
10.3%
t 11
 
9.5%
i 10
 
8.6%
n 9
 
7.8%
s 8
 
6.9%
o 8
 
6.9%
u 5
 
4.3%
P 4
 
3.4%
a 4
 
3.4%
Other values (21) 32
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 116
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 13
11.2%
e 12
 
10.3%
t 11
 
9.5%
i 10
 
8.6%
n 9
 
7.8%
s 8
 
6.9%
o 8
 
6.9%
u 5
 
4.3%
P 4
 
3.4%
a 4
 
3.4%
Other values (21) 32
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 116
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 13
11.2%
e 12
 
10.3%
t 11
 
9.5%
i 10
 
8.6%
n 9
 
7.8%
s 8
 
6.9%
o 8
 
6.9%
u 5
 
4.3%
P 4
 
3.4%
a 4
 
3.4%
Other values (21) 32
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 116
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 13
11.2%
e 12
 
10.3%
t 11
 
9.5%
i 10
 
8.6%
n 9
 
7.8%
s 8
 
6.9%
o 8
 
6.9%
u 5
 
4.3%
P 4
 
3.4%
a 4
 
3.4%
Other values (21) 32
27.6%

current_insurance_provider
Categorical

High correlation 

Distinct9
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Techniker Krankenkasse
10 
AOK Bayern
Barmer
DAK-Gesundheit
Allianz Private
Other values (4)
20 

Length

Max length22
Median length12
Mean length12.3
Min length3

Characters and Unicode

Total characters615
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTechniker Krankenkasse
2nd rowAOK Bayern
3rd rowBarmer
4th rowDAK-Gesundheit
5th rowAllianz Private

Common Values

ValueCountFrequency (%)
Techniker Krankenkasse 10
20.0%
AOK Bayern 5
10.0%
Barmer 5
10.0%
DAK-Gesundheit 5
10.0%
Allianz Private 5
10.0%
AOK Nordrhein 5
10.0%
BKK VBU 5
10.0%
IKK classic 5
10.0%
HEK 5
10.0%

Length

2025-06-03T16:23:50.014557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T16:23:50.077681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
techniker 10
11.8%
krankenkasse 10
11.8%
aok 10
11.8%
bayern 5
 
5.9%
barmer 5
 
5.9%
dak-gesundheit 5
 
5.9%
allianz 5
 
5.9%
private 5
 
5.9%
nordrhein 5
 
5.9%
bkk 5
 
5.9%
Other values (4) 20
23.5%

Most occurring characters

ValueCountFrequency (%)
e 70
 
11.4%
n 50
 
8.1%
K 50
 
8.1%
r 50
 
8.1%
a 45
 
7.3%
35
 
5.7%
s 35
 
5.7%
i 35
 
5.7%
k 30
 
4.9%
h 20
 
3.3%
Other values (24) 195
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 70
 
11.4%
n 50
 
8.1%
K 50
 
8.1%
r 50
 
8.1%
a 45
 
7.3%
35
 
5.7%
s 35
 
5.7%
i 35
 
5.7%
k 30
 
4.9%
h 20
 
3.3%
Other values (24) 195
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 70
 
11.4%
n 50
 
8.1%
K 50
 
8.1%
r 50
 
8.1%
a 45
 
7.3%
35
 
5.7%
s 35
 
5.7%
i 35
 
5.7%
k 30
 
4.9%
h 20
 
3.3%
Other values (24) 195
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 70
 
11.4%
n 50
 
8.1%
K 50
 
8.1%
r 50
 
8.1%
a 45
 
7.3%
35
 
5.7%
s 35
 
5.7%
i 35
 
5.7%
k 30
 
4.9%
h 20
 
3.3%
Other values (24) 195
31.7%

fitness_level
Categorical

High correlation 

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
Intermediate
24 
Advanced
15 
Beginner
11 

Length

Max length12
Median length8
Mean length9.92
Min length8

Characters and Unicode

Total characters496
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIntermediate
2nd rowAdvanced
3rd rowBeginner
4th rowIntermediate
5th rowBeginner

Common Values

ValueCountFrequency (%)
Intermediate 24
48.0%
Advanced 15
30.0%
Beginner 11
22.0%

Length

2025-06-03T16:23:50.157667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T16:23:50.228590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
intermediate 24
48.0%
advanced 15
30.0%
beginner 11
22.0%

Most occurring characters

ValueCountFrequency (%)
e 109
22.0%
n 61
12.3%
d 54
10.9%
t 48
9.7%
a 39
 
7.9%
r 35
 
7.1%
i 35
 
7.1%
I 24
 
4.8%
m 24
 
4.8%
A 15
 
3.0%
Other values (4) 52
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 109
22.0%
n 61
12.3%
d 54
10.9%
t 48
9.7%
a 39
 
7.9%
r 35
 
7.1%
i 35
 
7.1%
I 24
 
4.8%
m 24
 
4.8%
A 15
 
3.0%
Other values (4) 52
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 109
22.0%
n 61
12.3%
d 54
10.9%
t 48
9.7%
a 39
 
7.9%
r 35
 
7.1%
i 35
 
7.1%
I 24
 
4.8%
m 24
 
4.8%
A 15
 
3.0%
Other values (4) 52
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 109
22.0%
n 61
12.3%
d 54
10.9%
t 48
9.7%
a 39
 
7.9%
r 35
 
7.1%
i 35
 
7.1%
I 24
 
4.8%
m 24
 
4.8%
A 15
 
3.0%
Other values (4) 52
10.5%

resting_heart_rate
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.48
Minimum61
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:23:50.274867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile62.45
Q165.25
median68
Q371
95-th percentile75.55
Maximum77
Range16
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation4.031711
Coefficient of variation (CV)0.058874285
Kurtosis-0.59835338
Mean68.48
Median Absolute Deviation (MAD)3
Skewness0.24119504
Sum3424
Variance16.254694
MonotonicityNot monotonic
2025-06-03T16:23:50.341056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
68 7
14.0%
70 5
10.0%
69 4
 
8.0%
65 4
 
8.0%
67 4
 
8.0%
63 3
 
6.0%
71 3
 
6.0%
64 3
 
6.0%
66 3
 
6.0%
74 2
 
4.0%
Other values (7) 12
24.0%
ValueCountFrequency (%)
61 1
 
2.0%
62 2
 
4.0%
63 3
6.0%
64 3
6.0%
65 4
8.0%
66 3
6.0%
67 4
8.0%
68 7
14.0%
69 4
8.0%
70 5
10.0%
ValueCountFrequency (%)
77 1
 
2.0%
76 2
 
4.0%
75 2
 
4.0%
74 2
 
4.0%
73 2
 
4.0%
72 2
 
4.0%
71 3
6.0%
70 5
10.0%
69 4
8.0%
68 7
14.0%

blood_pressure_systolic
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.98
Minimum115
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:23:50.407307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum115
5-th percentile117.45
Q1120.25
median124
Q3127.75
95-th percentile135.55
Maximum140
Range25
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation5.8814478
Coefficient of variation (CV)0.047059112
Kurtosis-0.014981234
Mean124.98
Median Absolute Deviation (MAD)4
Skewness0.67069793
Sum6249
Variance34.591429
MonotonicityNot monotonic
2025-06-03T16:23:50.457696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
125 5
 
10.0%
124 5
 
10.0%
120 4
 
8.0%
122 3
 
6.0%
126 3
 
6.0%
123 3
 
6.0%
119 3
 
6.0%
118 3
 
6.0%
127 3
 
6.0%
121 2
 
4.0%
Other values (14) 16
32.0%
ValueCountFrequency (%)
115 1
 
2.0%
116 1
 
2.0%
117 1
 
2.0%
118 3
6.0%
119 3
6.0%
120 4
8.0%
121 2
 
4.0%
122 3
6.0%
123 3
6.0%
124 5
10.0%
ValueCountFrequency (%)
140 1
2.0%
138 1
2.0%
136 1
2.0%
135 2
4.0%
134 1
2.0%
133 1
2.0%
132 1
2.0%
131 1
2.0%
130 1
2.0%
129 1
2.0%

blood_pressure_diastolic
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.42
Minimum74
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:23:50.524475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile75.45
Q178.25
median81
Q383.75
95-th percentile88.55
Maximum91
Range17
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation4.1061924
Coefficient of variation (CV)0.050432233
Kurtosis-0.35350268
Mean81.42
Median Absolute Deviation (MAD)3
Skewness0.40049326
Sum4071
Variance16.860816
MonotonicityNot monotonic
2025-06-03T16:23:50.591065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
80 6
12.0%
82 6
12.0%
81 5
10.0%
83 4
 
8.0%
78 4
 
8.0%
76 3
 
6.0%
77 3
 
6.0%
79 3
 
6.0%
75 2
 
4.0%
85 2
 
4.0%
Other values (8) 12
24.0%
ValueCountFrequency (%)
74 1
 
2.0%
75 2
 
4.0%
76 3
6.0%
77 3
6.0%
78 4
8.0%
79 3
6.0%
80 6
12.0%
81 5
10.0%
82 6
12.0%
83 4
8.0%
ValueCountFrequency (%)
91 1
 
2.0%
90 1
 
2.0%
89 1
 
2.0%
88 2
 
4.0%
87 2
 
4.0%
86 2
 
4.0%
85 2
 
4.0%
84 2
 
4.0%
83 4
8.0%
82 6
12.0%

cholesterol_total
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.8
Minimum163
Maximum245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:23:50.657732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum163
5-th percentile165.45
Q1172.25
median177.5
Q3184.5
95-th percentile213.25
Maximum245
Range82
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation16.694127
Coefficient of variation (CV)0.091826881
Kurtosis5.9229624
Mean181.8
Median Absolute Deviation (MAD)5.5
Skewness2.2564152
Sum9090
Variance278.69388
MonotonicityNot monotonic
2025-06-03T16:23:50.711725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
180 3
 
6.0%
175 3
 
6.0%
176 3
 
6.0%
178 3
 
6.0%
177 3
 
6.0%
172 2
 
4.0%
165 2
 
4.0%
168 2
 
4.0%
174 2
 
4.0%
182 2
 
4.0%
Other values (22) 25
50.0%
ValueCountFrequency (%)
163 1
2.0%
165 2
4.0%
166 1
2.0%
167 1
2.0%
168 2
4.0%
169 1
2.0%
170 2
4.0%
171 1
2.0%
172 2
4.0%
173 1
2.0%
ValueCountFrequency (%)
245 1
2.0%
240 1
2.0%
220 1
2.0%
205 1
2.0%
200 1
2.0%
198 1
2.0%
195 2
4.0%
192 1
2.0%
190 1
2.0%
188 1
2.0%

glucose_level
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.36
Minimum86
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:23:50.774381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum86
5-th percentile87
Q190
median93
Q396
95-th percentile104.1
Maximum125
Range39
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.6846153
Coefficient of variation (CV)0.07084162
Kurtosis8.2718899
Mean94.36
Median Absolute Deviation (MAD)3
Skewness2.3014575
Sum4718
Variance44.684082
MonotonicityNot monotonic
2025-06-03T16:23:50.841131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
92 5
 
10.0%
94 5
 
10.0%
90 4
 
8.0%
95 4
 
8.0%
93 4
 
8.0%
88 3
 
6.0%
87 3
 
6.0%
89 3
 
6.0%
96 3
 
6.0%
91 3
 
6.0%
Other values (10) 13
26.0%
ValueCountFrequency (%)
86 1
 
2.0%
87 3
6.0%
88 3
6.0%
89 3
6.0%
90 4
8.0%
91 3
6.0%
92 5
10.0%
93 4
8.0%
94 5
10.0%
95 4
8.0%
ValueCountFrequency (%)
125 1
 
2.0%
110 1
 
2.0%
105 1
 
2.0%
103 1
 
2.0%
102 2
4.0%
101 1
 
2.0%
99 1
 
2.0%
98 2
4.0%
97 2
4.0%
96 3
6.0%
Distinct46
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
Minimum2023-07-10 00:00:00
Maximum2023-09-22 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-03T16:23:50.907721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:50.974434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)

medications
Text

Missing 

Distinct13
Distinct (%)92.9%
Missing36
Missing (%)72.0%
Memory size2.2 KiB
2025-06-03T16:23:51.074566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length11.5
Mean length10.714286
Min length8

Characters and Unicode

Total characters150
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)85.7%

Sample

1st rowLisinopril
2nd rowMetformin
3rd rowSalbutamol
4th rowIbuprofen
5th rowLevothyroxine
ValueCountFrequency (%)
levothyroxine 2
13.3%
lisinopril 1
 
6.7%
metformin 1
 
6.7%
salbutamol 1
 
6.7%
ibuprofen 1
 
6.7%
naproxen 1
 
6.7%
sumatriptan 1
 
6.7%
pregabalin 1
 
6.7%
sertraline 1
 
6.7%
cpap 1
 
6.7%
Other values (4) 4
26.7%
2025-06-03T16:23:51.244631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 14
 
9.3%
a 14
 
9.3%
t 13
 
8.7%
e 13
 
8.7%
n 12
 
8.0%
o 12
 
8.0%
i 11
 
7.3%
l 7
 
4.7%
p 6
 
4.0%
m 4
 
2.7%
Other values (21) 44
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 14
 
9.3%
a 14
 
9.3%
t 13
 
8.7%
e 13
 
8.7%
n 12
 
8.0%
o 12
 
8.0%
i 11
 
7.3%
l 7
 
4.7%
p 6
 
4.0%
m 4
 
2.7%
Other values (21) 44
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 14
 
9.3%
a 14
 
9.3%
t 13
 
8.7%
e 13
 
8.7%
n 12
 
8.0%
o 12
 
8.0%
i 11
 
7.3%
l 7
 
4.7%
p 6
 
4.0%
m 4
 
2.7%
Other values (21) 44
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 14
 
9.3%
a 14
 
9.3%
t 13
 
8.7%
e 13
 
8.7%
n 12
 
8.0%
o 12
 
8.0%
i 11
 
7.3%
l 7
 
4.7%
p 6
 
4.0%
m 4
 
2.7%
Other values (21) 44
29.3%

smoking_status
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
Non-smoker
42 
Ex-smoker
Smoker
 
3

Length

Max length10
Median length10
Mean length9.66
Min length6

Characters and Unicode

Total characters483
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon-smoker
2nd rowNon-smoker
3rd rowNon-smoker
4th rowNon-smoker
5th rowEx-smoker

Common Values

ValueCountFrequency (%)
Non-smoker 42
84.0%
Ex-smoker 5
 
10.0%
Smoker 3
 
6.0%

Length

2025-06-03T16:23:51.324443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T16:23:51.374603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
non-smoker 42
84.0%
ex-smoker 5
 
10.0%
smoker 3
 
6.0%

Most occurring characters

ValueCountFrequency (%)
o 92
19.0%
e 50
10.4%
k 50
10.4%
m 50
10.4%
r 50
10.4%
s 47
9.7%
- 47
9.7%
N 42
8.7%
n 42
8.7%
E 5
 
1.0%
Other values (2) 8
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 92
19.0%
e 50
10.4%
k 50
10.4%
m 50
10.4%
r 50
10.4%
s 47
9.7%
- 47
9.7%
N 42
8.7%
n 42
8.7%
E 5
 
1.0%
Other values (2) 8
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 92
19.0%
e 50
10.4%
k 50
10.4%
m 50
10.4%
r 50
10.4%
s 47
9.7%
- 47
9.7%
N 42
8.7%
n 42
8.7%
E 5
 
1.0%
Other values (2) 8
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 92
19.0%
e 50
10.4%
k 50
10.4%
m 50
10.4%
r 50
10.4%
s 47
9.7%
- 47
9.7%
N 42
8.7%
n 42
8.7%
E 5
 
1.0%
Other values (2) 8
 
1.7%

alcohol_consumption
Categorical

High correlation 

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Moderate
23 
Low
18 
High

Length

Max length8
Median length4
Mean length5.48
Min length3

Characters and Unicode

Total characters274
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerate
2nd rowLow
3rd rowModerate
4th rowLow
5th rowHigh

Common Values

ValueCountFrequency (%)
Moderate 23
46.0%
Low 18
36.0%
High 9
 
18.0%

Length

2025-06-03T16:23:51.445497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T16:23:51.491141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
moderate 23
46.0%
low 18
36.0%
high 9
 
18.0%

Most occurring characters

ValueCountFrequency (%)
e 46
16.8%
o 41
15.0%
M 23
8.4%
d 23
8.4%
r 23
8.4%
a 23
8.4%
t 23
8.4%
L 18
 
6.6%
w 18
 
6.6%
H 9
 
3.3%
Other values (3) 27
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 274
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 46
16.8%
o 41
15.0%
M 23
8.4%
d 23
8.4%
r 23
8.4%
a 23
8.4%
t 23
8.4%
L 18
 
6.6%
w 18
 
6.6%
H 9
 
3.3%
Other values (3) 27
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 274
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 46
16.8%
o 41
15.0%
M 23
8.4%
d 23
8.4%
r 23
8.4%
a 23
8.4%
t 23
8.4%
L 18
 
6.6%
w 18
 
6.6%
H 9
 
3.3%
Other values (3) 27
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 274
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 46
16.8%
o 41
15.0%
M 23
8.4%
d 23
8.4%
r 23
8.4%
a 23
8.4%
t 23
8.4%
L 18
 
6.6%
w 18
 
6.6%
H 9
 
3.3%
Other values (3) 27
9.9%

sleep_hours_avg
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.526
Minimum6.2
Maximum8.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:23:51.541142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile6.4
Q17.325
median7.7
Q38
95-th percentile8.2
Maximum8.3
Range2.1
Interquartile range (IQR)0.675

Descriptive statistics

Standard deviation0.58442716
Coefficient of variation (CV)0.077654419
Kurtosis-0.32084179
Mean7.526
Median Absolute Deviation (MAD)0.3
Skewness-0.88535022
Sum376.3
Variance0.3415551
MonotonicityNot monotonic
2025-06-03T16:23:51.601410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
8 6
12.0%
7.8 5
10.0%
8.1 5
10.0%
7.5 4
 
8.0%
7.7 4
 
8.0%
7.6 4
 
8.0%
7.4 3
 
6.0%
8.2 3
 
6.0%
6.5 2
 
4.0%
7.9 2
 
4.0%
Other values (10) 12
24.0%
ValueCountFrequency (%)
6.2 1
2.0%
6.3 1
2.0%
6.4 2
4.0%
6.5 2
4.0%
6.6 1
2.0%
6.7 1
2.0%
6.8 2
4.0%
7 1
2.0%
7.2 1
2.0%
7.3 1
2.0%
ValueCountFrequency (%)
8.3 1
 
2.0%
8.2 3
6.0%
8.1 5
10.0%
8 6
12.0%
7.9 2
 
4.0%
7.8 5
10.0%
7.7 4
8.0%
7.6 4
8.0%
7.5 4
8.0%
7.4 3
6.0%

exercise_frequency_per_week
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.72
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:23:51.641209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4851647
Coefficient of variation (CV)0.39923783
Kurtosis-0.73827842
Mean3.72
Median Absolute Deviation (MAD)1
Skewness-0.27330506
Sum186
Variance2.2057143
MonotonicityNot monotonic
2025-06-03T16:23:51.710552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 14
28.0%
5 10
20.0%
3 9
18.0%
6 6
12.0%
2 6
12.0%
1 5
 
10.0%
ValueCountFrequency (%)
1 5
 
10.0%
2 6
12.0%
3 9
18.0%
4 14
28.0%
5 10
20.0%
6 6
12.0%
ValueCountFrequency (%)
6 6
12.0%
5 10
20.0%
4 14
28.0%
3 9
18.0%
2 6
12.0%
1 5
 
10.0%

Interactions

2025-06-03T16:23:47.496864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:42.355896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:42.857424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.413158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.975668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:44.564451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:45.505270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.019862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.507568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.021015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.540991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:42.396066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:42.910215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.471174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:44.026114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:44.619484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:45.550385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.061098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.559435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.060235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.590860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:42.450086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:42.962016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.524811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:44.082367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:44.676664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:45.604711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.107437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.607486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.107566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.641015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:42.501548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.009293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.577036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:44.141091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:44.731170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:45.659380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.158090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.662863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.157232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.690984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:42.554624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.074078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.630096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:44.194703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:45.175629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:45.710824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.207982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.707395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.207293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.740954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:42.603708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.131437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.690990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:44.259982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:45.224897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:45.757055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.257415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.757148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.253375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.790642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:42.656013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.192187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.757138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:44.330532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:45.279281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:45.810869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.307471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.807415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.299984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.843345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:42.707530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.247046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.807845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:44.384255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:45.324284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:45.857194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.359860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.871133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.357625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.890929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:42.757098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.306627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.864895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:44.444386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:45.396977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:45.925537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.414803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.924232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.409878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.938765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:42.805058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.362288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:43.915494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:44.495009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:45.453299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:45.971375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.460867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:46.970387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:23:47.451857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-06-03T16:23:51.757910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
alcohol_consumptionblood_pressure_diastolicblood_pressure_systolicblood_typebmicholesterol_totalcurrent_insurance_providerexercise_frequency_per_weekfitness_levelglucose_levelheight_cmresting_heart_ratesleep_hours_avgsmoking_statusweight_kg
alcohol_consumption1.0000.4400.5130.2530.5650.4250.0000.4400.4050.3950.3690.3690.4490.4540.538
blood_pressure_diastolic0.4401.0000.9790.1420.6240.9640.000-0.8910.8960.9500.3990.958-0.9430.5360.535
blood_pressure_systolic0.5130.9791.0000.0900.5850.9590.000-0.8880.8140.9400.3740.956-0.9290.7640.506
blood_type0.2530.1420.0901.0000.1370.0000.5650.1470.0640.0000.2550.0760.0000.0550.100
bmi0.5650.6240.5850.1371.0000.6020.286-0.5160.4920.5430.8550.565-0.5790.6390.959
cholesterol_total0.4250.9640.9590.0000.6021.0000.000-0.8840.7740.9870.4150.942-0.9580.8190.529
current_insurance_provider0.0000.0000.0000.5650.2860.0001.0000.0000.0000.0000.3670.0000.0000.0000.210
exercise_frequency_per_week0.440-0.891-0.8880.147-0.516-0.8840.0001.0000.941-0.883-0.244-0.8960.8780.614-0.404
fitness_level0.4050.8960.8140.0640.4920.7740.0000.9411.0000.7760.0520.8290.8750.5560.445
glucose_level0.3950.9500.9400.0000.5430.9870.000-0.8830.7761.0000.3530.934-0.9530.7870.471
height_cm0.3690.3990.3740.2550.8550.4150.367-0.2440.0520.3531.0000.342-0.3890.1300.948
resting_heart_rate0.3690.9580.9560.0760.5650.9420.000-0.8960.8290.9340.3421.000-0.9210.6350.479
sleep_hours_avg0.449-0.943-0.9290.000-0.579-0.9580.0000.8780.875-0.953-0.389-0.9211.0000.549-0.510
smoking_status0.4540.5360.7640.0550.6390.8190.0000.6140.5560.7870.1300.6350.5491.0000.677
weight_kg0.5380.5350.5060.1000.9590.5290.210-0.4040.4450.4710.9480.479-0.5100.6771.000

Missing values

2025-06-03T16:23:48.007530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-03T16:23:48.391197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-03T16:23:48.491570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

user_idheight_cmweight_kgbmiblood_typemedical_conditionsallergiescurrent_insurance_providerfitness_levelresting_heart_rateblood_pressure_systolicblood_pressure_diastoliccholesterol_totalglucose_levellast_medical_checkupmedicationssmoking_statusalcohol_consumptionsleep_hours_avgexercise_frequency_per_week
0USR0011787222.7A+NaNPollenTechniker KrankenkasseIntermediate6812080180952023-08-15NaNNon-smokerModerate7.54
1USR0021655821.3O-NaNNaNAOK BayernAdvanced6211575165882023-09-20NaNNon-smokerLow8.06
2USR0031828525.7B+HypertensionShellfishBarmerBeginner75140902201022023-07-10LisinoprilNon-smokerModerate6.52
3USR0041706321.8AB+NaNNutsDAK-GesundheitIntermediate7012582175922023-09-05NaNNon-smokerLow7.85
4USR0051757825.5A-Diabetes Type 2NaNAllianz PrivateBeginner72135852401252023-08-30MetforminEx-smokerHigh6.81
5USR0061686121.6O+NaNLatexTechniker KrankenkasseAdvanced6411878170892023-09-12NaNNon-smokerLow8.25
6USR0071807623.5A+NaNNaNAOK NordrheinIntermediate6912883185962023-08-25NaNNon-smokerModerate7.44
7USR0081625521.0B-AsthmaDust mitesBKK VBUIntermediate6612279172912023-09-18SalbutamolNon-smokerLow7.93
8USR0091768226.5O+Lower back painNaNIKK classicBeginner7413288195982023-07-28IbuprofenSmokerHigh6.22
9USR0101726421.6AB-NaNCatsHEKAdvanced6311976168872023-09-08NaNNon-smokerModerate8.16
user_idheight_cmweight_kgbmiblood_typemedical_conditionsallergiescurrent_insurance_providerfitness_levelresting_heart_rateblood_pressure_systolicblood_pressure_diastoliccholesterol_totalglucose_levellast_medical_checkupmedicationssmoking_statusalcohol_consumptionsleep_hours_avgexercise_frequency_per_week
40USR0411798225.6AB-NaNNaNTechniker KrankenkasseIntermediate6812481178932023-08-21NaNNon-smokerHigh7.64
41USR0421665921.4O+Celiac diseaseGlutenAOK BayernIntermediate6912582179952023-09-09NaNNon-smokerModerate7.74
42USR0431788025.2B+NaNNaNBarmerBeginner7313186188982023-07-27NaNSmokerHigh6.32
43USR0441706221.5A-NaNLatexDAK-GesundheitAdvanced6311977168892023-09-21NaNNon-smokerLow8.05
44USR0451757424.2O-NaNNaNIKK classicIntermediate7112784182962023-08-16NaNNon-smokerModerate7.53
45USR0461686021.3AB+PrediabetesNaNTechniker KrankenkasseIntermediate70128831951102023-09-02NaNNon-smokerModerate7.34
46USR0471808425.9A+NaNNaNBKK VBUBeginner76134872001022023-07-24NaNEx-smokerHigh6.41
47USR0481645721.2B-NaNPollenAOK NordrheinAdvanced6512078171902023-09-12NaNNon-smokerLow8.15
48USR0491777824.9O+NaNNaNHEKIntermediate6712280176922023-08-27NaNNon-smokerModerate7.84
49USR0501655821.3A-HypothyroidismIodineAllianz PrivateIntermediate6812481177942023-09-05LevothyroxineNon-smokerLow7.63